AIMC Topic: Polysomnography

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CRT: A Convolutional Recurrent Transformer for Automatic Sleep State Detection.

IEEE journal of biomedical and health informatics
Sleep is a crucial period of rest necessary for optimal cognitive function, psychological well-being, and execution of everyday tasks. In the field of sleep healthcare, the primary objective is to identify and classify the various sleep states. Imple...

Diagnostic accuracy of machine learning algorithms in electrocardiogram-based sleep apnea detection: A systematic review and meta-analysis.

Sleep medicine reviews
Sleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underdiagnosed due to the limitations of polysomnography (PSG), the current diagnostic gold standard. Single-lead electrocardiography (ECG) has been propose...

A case study on generative artificial intelligence to extract the fundamental sleep parameters from polysomnography notes.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
UNLABELLED: Generative artificial intelligence utilizing transformer technology is widely seen as a groundbreaking advancement in applied artificial intelligence. The technology creates a unique opportunity to extract unstructured data from medical n...

Wearable Sensors and Artificial Intelligence for Sleep Apnea Detection: A Systematic Review.

Journal of medical systems
Sleep apnea, a prevalent disorder affecting millions of people worldwide, has attracted increasing attention in recent years due to its significant impact on public health and quality of life. The integration of wearable devices and artificial intell...

Automating Data Extraction from PDF Sleep Reports Using Data Mining Techniques.

Studies in health technology and informatics
This work introduces a web application for extracting, processing, and visualizing data from sleep studies reports. Using Optical Character Recognition (OCR) and Natural Language Processing (NLP), the pipeline extracts over 75 key data points from fo...

Revolutionizing sleep disorder diagnosis: A Multi-Task learning approach optimized with genetic and Q-Learning techniques.

Scientific reports
Adequate sleep is crucial for maintaining a healthy lifestyle, and its deficiency can lead to various sleep-related disorders. Identifying these disorders early is essential for effective treatment, which traditionally relies on polysomnogram (PSG) t...

Validation of a fingertip home sleep apnea testing system using deep learning AI and a temporal event localization analysis.

Sleep
STUDY OBJECTIVES: This paper validates TipTraQ, a compact home sleep apnea testing (HSAT) system. TipTraQ comprises a fingertip-worn device, a mobile application, and a cloud-based deep learning artificial intelligence (AI) system. The device utilize...

A novel machine learning model for screening the risk of obstructive sleep apnea using craniofacial photography with questionnaires.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
STUDY OBJECTIVES: Undiagnosed or untreated moderate-to-severe obstructive sleep apnea (OSA) increases cardiovascular risks and mortality. Early and efficient detection is critical, given its high prevalence. We aimed to develop a practical and effici...

The combination of physiology and machine learning for prediction of CPAP pressure and residual AHI in OSA.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
STUDY OBJECTIVES: Continuous positive airway pressure (CPAP) is the treatment of choice for obstructive sleep apnea; however, some people have residual respiratory events or require significantly higher CPAP pressure while on therapy. Our objective w...

Mortality risk assessment using deep learning-based frequency analysis of electroencephalography and electrooculography in sleep.

Sleep
STUDY OBJECTIVES: To assess whether the frequency content of electroencephalography (EEG) and electrooculography (EOG) during nocturnal polysomnography (PSG) can predict all-cause mortality.